A novel discrete bat algorithm for solving the travelling salesman problem

2015 ◽  
Vol 27 (7) ◽  
pp. 1853-1866 ◽  
Author(s):  
Yassine Saji ◽  
Mohammed Essaid Riffi
Keyword(s):  
Travelling Salesman Problem ◽  
Bat Algorithm ◽  
Travelling Salesman

scholarly journals Penerapan Bat Algorithm Dalam Penyelsaian Kasus Travelling Salesman Problem (TSP) Pada Internship Program

2021 ◽  
Vol 6 (2) ◽  
pp. 111-116
Author(s):  
Veri Julianto ◽  
Hendrik Setyo Utomo ◽  
Muhammad Rusyadi Arrahimi
Keyword(s):  
Global Minimum ◽  
Nearest Neighbor ◽  
Travelling Salesman Problem ◽  
Bat Algorithm ◽  
Travelling Salesman ◽  
Street Vendors ◽  
Shortest Distance ◽  
Using Data ◽  
Feasible Solutions ◽  
The Traveling Salesman Problem

This optimization is an optimization case that organizes all possible and feasible solutions in discrete form. One form of combinatorial optimization that can be used as material in testing a method is the Traveling Salesman Problem (TSP). In this study, the bat algorithm will be used to find the optimum value in TSP. Utilization of the Metaheuristic Algorithm through the concept of the Bat Algorithm is able to provide optimal results in searching for the shortest distance in the case of TSP. Based on trials conducted using data on the location of student street vendors, the Bat Algortima is able to obtain the global minimum or the shortest distance when compared to the nearest neighbor method, Hungarian method, branch and bound method.

scholarly journals Tourist Route Optimization in the Context of Covid-19 Pandemic

2021 ◽  
Vol 13 (10) ◽  
pp. 5492
Author(s):  
Cristina Maria Păcurar ◽  
Ruxandra-Gabriela Albu ◽  
Victor Dan Păcurar
Keyword(s):  
Travelling Salesman Problem ◽  
Route Planning ◽  
Route Optimization ◽  
Travelling Salesman ◽  
Social Distancing ◽  
Tourist Destinations ◽  
Innovative Method ◽  
The Social ◽  
Immediate Response

The paper presents an innovative method for tourist route planning inside a destination. The necessity of reorganizing the tourist routes within a destination comes as an immediate response to the Covid-19 crisis. The implementation of the method inside tourist destinations can bring an important advantage in transforming a destination into a safer one in times of Covid-19 and post-Covid-19. The existing trend of shortening the tourist stay length has been accelerated while the epidemic became a pandemic. Moreover, the wariness for future pandemics has brought into spotlight the issue of overcrowded attractions inside a destination at certain moments. The method presented in this paper proposes a backtracking algorithm, more precisely an adaptation of the travelling salesman problem. The method presented is aimed to facilitate the navigation inside a destination and to revive certain less-visited sightseeing spots inside a destination while facilitating conformation with the social distancing measures imposed for Covid-19 control.

A column-and-row generation approach for the flying sidekick travelling salesman problem

2021 ◽  
Vol 124 ◽  
pp. 102913
Author(s):  
Maurizio Boccia ◽  
Adriano Masone ◽  
Antonio Sforza ◽  
Claudio Sterle
Keyword(s):  
Travelling Salesman Problem ◽  
Travelling Salesman

scholarly journals Converting MST to TSP Path by Branch Elimination

2020 ◽  
Vol 11 (1) ◽  
pp. 177
Author(s):  
Pasi Fränti ◽  
Teemu Nenonen ◽  
Mingchuan Yuan
Keyword(s):  
Spanning Tree ◽  
Closed Loop ◽  
Minimum Spanning Tree ◽  
Travelling Salesman Problem ◽  
Open Loop ◽  
Travelling Salesman ◽  
Elimination Algorithm ◽  
Number Of Iterations ◽  
Number Of Branches

Travelling salesman problem (TSP) has been widely studied for the classical closed loop variant but less attention has been paid to the open loop variant. Open loop solution has property of being also a spanning tree, although not necessarily the minimum spanning tree (MST). In this paper, we present a simple branch elimination algorithm that removes the branches from MST by cutting one link and then reconnecting the resulting subtrees via selected leaf nodes. The number of iterations equals to the number of branches (b) in the MST. Typically, b << n where n is the number of nodes. With O-Mopsi and Dots datasets, the algorithm reaches gap of 1.69% and 0.61 %, respectively. The algorithm is suitable especially for educational purposes by showing the connection between MST and TSP, but it can also serve as a quick approximation for more complex metaheuristics whose efficiency relies on quality of the initial solution.

Sign in / Sign up

Export Citation Format

Share Document